(COVID-19 data Exploration)

by (Nesma Lotfy)

Investigation Overview

we need to explore if increasing the number of strategies will yield to decrease of number of cases of covid-19 or not in other meaning is it was useful to increase the banning strategies to decrease the cases. .

Dataset Overview

There are 1537 rows in the dataset with 17 features ( country, ID, week number, year, year-week, level, region, region_name, population, testing_rate (tests done/population100000), positivity_rate (new cases/tests done 100), cases (new cases), rate_14_day (14-day notification rate of reported COVID-19 cases per 100 000 population), cumulative_count (of cases), deaths, rate_14_day.1 (14-day notification rate of reported deaths per 1 000 000 population), cumulative_count.1 (of deaths), strategy_count (total number of strategies used per week in each country), this data is collected per week for each country during 2020

Note that the above cells have been set as "Skip"-type slides. That means that when the notebook is rendered as http slides, those cells won't show up.

(Distribution of deaths and cases of COVID-19 )

Around 2.3% of COVID-19 cases died and 97.6% survived

(-1.1086734519537436,
 1.1047067839031974,
 -1.1071276828223413,
 1.1062525530345997)

(Distribution of cases_rate and death rate of COVID-19 for each country)

case rate is calcauted as number of new cases divided by population of each country

death rate is calcauted as number of deaths divided by cases of each country

from the graph we see that, Luxembourg has the maximum case rate while finalnad has the minimum, reagarding death rate Greece has the maximum death rate while hungary has the minimu

show the time series of cases regarding week for each country

almost all countries have peakes for positive rate at the beginning of the year or the last of the year such as France, belgium except plonad almost there is noo peak

<seaborn.axisgrid.FacetGrid at 0x24ae3285a00>

<seaborn.axisgrid.FacetGrid at 0x24ae3d67e80>

<seaborn.axisgrid.FacetGrid at 0x24ae3d55160>

<seaborn.axisgrid.FacetGrid at 0x24ae362f820>

<seaborn.axisgrid.FacetGrid at 0x24ae3dada90>

<seaborn.axisgrid.FacetGrid at 0x24ae3d97520>

<seaborn.axisgrid.FacetGrid at 0x24ae41ef7f0>

<seaborn.axisgrid.FacetGrid at 0x24ae392f3d0>

show the time series of deaths regarding week for each country

<seaborn.axisgrid.FacetGrid at 0x24ae3d44ee0>

<seaborn.axisgrid.FacetGrid at 0x24ae3da7dc0>

<seaborn.axisgrid.FacetGrid at 0x24ae3145eb0>

<seaborn.axisgrid.FacetGrid at 0x24ae34fd2b0>

<seaborn.axisgrid.FacetGrid at 0x24ae3570820>

<seaborn.axisgrid.FacetGrid at 0x24ae324a550>

<seaborn.axisgrid.FacetGrid at 0x24ae379a5e0>

<seaborn.axisgrid.FacetGrid at 0x24ae3ec8460>

(to see the effect of number of strategies used for each country on cases evrey week within the year. )

from the graphs we can conclude, at the end of the year there is an explosion of cases may be due that the number of strategies decresed before the explosions, an example for that case; Austria and Bulgaria. Some country doesnot have this role, an example, Iceland, the reason may be there is small number of cases regrading this country as shown by graph above.

Time series analysis

Effect of number of strategies used for each country on cases evrey week within the year

we can see that decrease number of stragies at some point yeild to increase the cases after that an example Austria, Bulgaria,czechia,....

Once you're ready to finish your presentation, check your output by using nbconvert to export the notebook and set up a server for the slides. From the terminal or command line, use the following expression:

jupyter nbconvert <file_name>.ipynb --to slides --post serve --template output_toggle

This should open a tab in your web browser where you can scroll through your presentation. Sub-slides can be accessed by pressing 'down' when viewing its parent slide. Make sure you remove all of the quote-formatted guide notes like this one before you finish your presentation!